Perceptual computing may be the ability of a computer to sense or analyze its environment, including users in that environment, and respond accordingly. Indeed, the computer may be able to determine what needs a user might have. Some perceptual computing applications offer a depth sensor and cameras. Perceptual computing may provide for facial analysis, hand and finger tracking, speech recognition, gesture recognition, body part recognition, posture analysis, scene analysis, object recognition, object measurement, and identification of user features such as eyes, mouth, and nose. Perceptual computing may provide for the computer to gain insights from users that can be applied to applications. Evaluating user emotions may be possible, such as by analyzing facial expression.
Anonymous video analytics (AVA) and image analytics are two types of perceptual computing analytics. AVA gathers metrics about audience engagement with an object or a host computing system such as a digital sign, a kiosk, or merchandise on a shelf. Image analytics is the extraction of meaningful information mainly from digital images using digital image processing techniques.
Furthermore, the Internet of Things (IoT) may bring Internet connectivity to as many as 50 billion devices by 2020. For organizations, IoT devices may provide opportunities for monitoring, tracking, or controlling other devices and items, including further IoT devices, other home and industrial devices, items in manufacturing and food production chains, and the like.
The same numbers are used throughout the disclosure and the figures to reference like components and features. Numbers in the 100 series refer to features originally found in FIG. 1; numbers in the 200 series refer to features originally found in FIG. 2; and so on.